{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**HyperTS支持解决单变量时序预测, 多变量时序预测任务。**\n",
    "\n",
    "**在本NoteBook中, 我们利用HyperTS做一个时序预测任务。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: 首先，我们导入示例数据集**network traffic**, 并做EDA分析数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts.datasets import load_network_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_network_traffic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Var_2</th>\n",
       "      <th>Var_3</th>\n",
       "      <th>Var_4</th>\n",
       "      <th>Var_5</th>\n",
       "      <th>Var_6</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01 00:00:00</td>\n",
       "      <td>0.7534</td>\n",
       "      <td>3.375</td>\n",
       "      <td>10.195</td>\n",
       "      <td>1.4490</td>\n",
       "      <td>19174.977</td>\n",
       "      <td>286443.880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-01 01:00:00</td>\n",
       "      <td>0.3376</td>\n",
       "      <td>2.414</td>\n",
       "      <td>3.920</td>\n",
       "      <td>0.4065</td>\n",
       "      <td>7529.263</td>\n",
       "      <td>178930.450</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-01 02:00:00</td>\n",
       "      <td>0.2032</td>\n",
       "      <td>1.654</td>\n",
       "      <td>3.318</td>\n",
       "      <td>0.2142</td>\n",
       "      <td>3310.539</td>\n",
       "      <td>42296.164</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-01 03:00:00</td>\n",
       "      <td>0.2420</td>\n",
       "      <td>1.393</td>\n",
       "      <td>3.148</td>\n",
       "      <td>0.2312</td>\n",
       "      <td>4535.464</td>\n",
       "      <td>26220.232</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-01 04:00:00</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>1.429</td>\n",
       "      <td>3.215</td>\n",
       "      <td>0.2157</td>\n",
       "      <td>2732.911</td>\n",
       "      <td>27990.348</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp   Var_1  Var_2   Var_3   Var_4      Var_5       Var_6  \\\n",
       "0  2021-03-01 00:00:00  0.7534  3.375  10.195  1.4490  19174.977  286443.880   \n",
       "1  2021-03-01 01:00:00  0.3376  2.414   3.920  0.4065   7529.263  178930.450   \n",
       "2  2021-03-01 02:00:00  0.2032  1.654   3.318  0.2142   3310.539   42296.164   \n",
       "3  2021-03-01 03:00:00  0.2420  1.393   3.148  0.2312   4535.464   26220.232   \n",
       "4  2021-03-01 04:00:00  0.1940  1.429   3.215  0.2157   2732.911   27990.348   \n",
       "\n",
       "    HourSin  WeekCos CBWD  \n",
       "0  0.000000      1.0   NW  \n",
       "1  0.258819      1.0   NW  \n",
       "2  0.500000      1.0   NW  \n",
       "3  0.707107      1.0   NW  \n",
       "4  0.866025      1.0   NW  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由数据我们可知：\n",
    "\n",
    "- 时间列名称: 'TimeStamp';\n",
    "- 目标列名称: ['Var_1', 'Var_2', 'Var_3', 'Var_4', 'Var_5', 'Var_6'];\n",
    "- 协变量列名称: ['HourSin', 'WeekCos', 'CBWD'];\n",
    "- 时间频率: 'H'.\n",
    "  \n",
    "由目标变量的具体数值可知，变量之间是存在量纲差异的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2878 entries, 0 to 2877\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TimeStamp  2878 non-null   object \n",
      " 1   Var_1      2878 non-null   float64\n",
      " 2   Var_2      2878 non-null   float64\n",
      " 3   Var_3      2877 non-null   float64\n",
      " 4   Var_4      2877 non-null   float64\n",
      " 5   Var_5      2877 non-null   float64\n",
      " 6   Var_6      2877 non-null   float64\n",
      " 7   HourSin    2878 non-null   float64\n",
      " 8   WeekCos    2878 non-null   float64\n",
      " 9   CBWD       2878 non-null   object \n",
      "dtypes: float64(8), object(2)\n",
      "memory usage: 225.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出, 这个数据是存在缺失值的。TimeStamp的类型是object。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time start: 2021-03-01 00:00:00 - time end: 2021-06-30 23:00:00\n",
      "complete_time_length: 2928 - actual_time_length: 2878\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "time_start = df['TimeStamp'].tolist()[0]\n",
    "time_end = df['TimeStamp'].tolist()[-1]\n",
    "\n",
    "complete_time_df = pd.date_range(start=time_start, end=time_end, freq='H')\n",
    "\n",
    "complete_time_length = len(complete_time_df)\n",
    "actual_time_length = len(df)\n",
    "\n",
    "print('time start: {} - time end: {}'.format(time_start, time_end))\n",
    "print('complete_time_length: {} - actual_time_length: {}'.format(complete_time_length, actual_time_length))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过计算可以看出，如果时间序列起始时间是2021-03-01 00:00:00, 结束时间是2021-06-30 23:00:00，以小时(H)为时间粒度，那么自然序列共有2928个时间点，但是实际上数据共有2878个时间点。因此，此数据在收集过程中，可能由于某些因素丢失了某些时间片段的值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们从中选择变量Var_3做为目标变量，做一个单变量预测，并分割数据为训练集和测试集。\n",
    "\n",
    "对于数据集的分割，我们可以使用sklearn.model_selection内置的```train_test_split```的函数，这里hyperts也提供了相应的函数```temporal_train_test_split```供使用。\n",
    "\n",
    "**任务目标:** 根据历史数据信息来预测未来168个时间点的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts.toolbox import temporal_train_test_split\n",
    "\n",
    "df = df.drop(['Var_1', 'Var_2','Var_4','Var_5','Var_6'], axis=1)\n",
    "\n",
    "train_data, test_data = temporal_train_test_split(df, test_horizon=168)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_3</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01 00:00:00</td>\n",
       "      <td>10.195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-01 01:00:00</td>\n",
       "      <td>3.920</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-01 02:00:00</td>\n",
       "      <td>3.318</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-01 03:00:00</td>\n",
       "      <td>3.148</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-01 04:00:00</td>\n",
       "      <td>3.215</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp   Var_3   HourSin  WeekCos CBWD\n",
       "0  2021-03-01 00:00:00  10.195  0.000000      1.0   NW\n",
       "1  2021-03-01 01:00:00   3.920  0.258819      1.0   NW\n",
       "2  2021-03-01 02:00:00   3.318  0.500000      1.0   NW\n",
       "3  2021-03-01 03:00:00   3.148  0.707107      1.0   NW\n",
       "4  2021-03-01 04:00:00   3.215  0.866025      1.0   NW"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_3</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>2021-06-30 18:00:00</td>\n",
       "      <td>10.530</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2874</th>\n",
       "      <td>2021-06-30 20:00:00</td>\n",
       "      <td>12.890</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>2021-06-30 21:00:00</td>\n",
       "      <td>12.920</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>2021-06-30 22:00:00</td>\n",
       "      <td>9.016</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>cv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>2021-06-30 23:00:00</td>\n",
       "      <td>9.970</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TimeStamp   Var_3   HourSin   WeekCos CBWD\n",
       "2873  2021-06-30 18:00:00  10.530 -1.000000 -0.222521   NW\n",
       "2874  2021-06-30 20:00:00  12.890 -0.866025 -0.222521   NW\n",
       "2875  2021-06-30 21:00:00  12.920 -0.707107 -0.222521   SE\n",
       "2876  2021-06-30 22:00:00   9.016 -0.500000 -0.222521   cv\n",
       "2877  2021-06-30 23:00:00   9.970 -0.258819 -0.222521   NE"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(pd.to_datetime(train_data['TimeStamp']), train_data['Var_3'], c='k', label='train data')\n",
    "plt.plot(pd.to_datetime(test_data['TimeStamp']), test_data['Var_3'], c='r', label='test data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: 创建实验并搜索模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 为了方便且避免定义参数书写错误, 我们可以使用```hyperts.utils```中的```consts```来获取符合条件的变量名。\n",
    "- 为了搜索到更好的模型，我们通过调整参数```max_trials```来增加搜索的次数。\n",
    "  同时避免陷入长时间的无效搜索，我们增加早停策略，及```early_stopping_rounds```限制不提升轮数和```early_stopping_time_limit```运行总时间。\n",
    "- HyperTS默认是进化搜索，我们可以通过调整参数```searcher```将其更改为蒙特卡洛树搜索。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts.utils import consts\n",
    "from hyperts import make_experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "experiment = make_experiment(train_data=train_data.copy(),\n",
    "                task=consts.Task_UNIVARIATE_FORECAST,\n",
    "                mode=consts.Mode_STATS,\n",
    "                timestamp='TimeStamp',\n",
    "                covariables=['HourSin', 'WeekCos', 'CBWD'],\n",
    "                max_trials=5,\n",
    "                early_stopping_rounds=10,\n",
    "                early_stopping_time_limit=3600,\n",
    "                reward_metric='mae',\n",
    "                searcher='mcts')\n",
    "\n",
    "model = experiment.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: 获得模型的参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Pipeline.get_params of Pipeline(steps=[('data_preprocessing',\n",
       "                 TSFDataPreprocessStep(covariate_cols=['HourSin', 'WeekCos',\n",
       "                                                       'CBWD'],\n",
       "                                       covariate_data_clean_args={'correct_object_dtype': False,\n",
       "                                                                  'drop_columns': None,\n",
       "                                                                  'drop_constant_columns': True,\n",
       "                                                                  'drop_duplicated_columns': False,\n",
       "                                                                  'drop_idness_columns': True,\n",
       "                                                                  'drop_label_nan_rows': True,\n",
       "                                                                  'int_convert_to': 'float',\n",
       "                                                                  'nan_chars': None,\n",
       "                                                                  'reduce_mem_usage': False,\n",
       "                                                                  'reserve_columns': None},\n",
       "                                       covariate_data_cleaner_args={'correct_object_dtype': False},\n",
       "                                       freq='H', name='data_preprocessing',\n",
       "                                       timestamp_col=['TimeStamp'])),\n",
       "                ('estimator',\n",
       "                 <hyperts.hyper_ts.HyperTSEstimator object at 0x000001F0297C0278>)])>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_pipeline_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: 对于测试数据(未知数据)进行预测推理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, y_test = model.split_X_y(test_data.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>2021-06-23 19:00:00</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>2021-06-23 20:00:00</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>cv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>2021-06-23 21:00:00</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>2021-06-23 22:00:00</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>2021-06-23 23:00:00</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TimeStamp   HourSin   WeekCos CBWD\n",
       "2710  2021-06-23 19:00:00 -0.965926 -0.222521   NW\n",
       "2711  2021-06-23 20:00:00 -0.866025 -0.222521   cv\n",
       "2712  2021-06-23 21:00:00 -0.707107 -0.222521   NW\n",
       "2713  2021-06-23 22:00:00 -0.500000 -0.222521   NW\n",
       "2714  2021-06-23 23:00:00 -0.258819 -0.222521   NW"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>2021-06-23 19:00:00</td>\n",
       "      <td>10.518559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>2021-06-23 20:00:00</td>\n",
       "      <td>10.883351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>2021-06-23 21:00:00</td>\n",
       "      <td>10.618050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>2021-06-23 22:00:00</td>\n",
       "      <td>9.270074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>2021-06-23 23:00:00</td>\n",
       "      <td>7.144689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TimeStamp      Var_3\n",
       "2710 2021-06-23 19:00:00  10.518559\n",
       "2711 2021-06-23 20:00:00  10.883351\n",
       "2712 2021-06-23 21:00:00  10.618050\n",
       "2713 2021-06-23 22:00:00   9.270074\n",
       "2714 2021-06-23 23:00:00   7.144689"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model.predict(X_test)\n",
    "forecast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HyperTS的```predict```将输出一个DataFrame, 其包含X_test中所有时间的目标变量预测值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: 在评估过程，我们可以采用HyperTS的```evaluate```函数，其对各种任务默认内置了一些评估指标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metirc</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>1.3301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mse</td>\n",
       "      <td>4.0493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmse</td>\n",
       "      <td>2.0123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mape</td>\n",
       "      <td>0.1549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>smape</td>\n",
       "      <td>0.1526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metirc  Score\n",
       "0    mae 1.3301\n",
       "1    mse 4.0493\n",
       "2   rmse 2.0123\n",
       "3   mape 0.1549\n",
       "4  smape 0.1526"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = model.evaluate(y_true=y_test, y_pred=forecast)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metirc</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean_absolute_error</td>\n",
       "      <td>1.3301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean_squared_error</td>\n",
       "      <td>4.0493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mape</td>\n",
       "      <td>0.1549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Metirc  Score\n",
       "0  mean_absolute_error 1.3301\n",
       "1   mean_squared_error 4.0493\n",
       "2                 mape 0.1549"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "results = model.evaluate(y_true=y_test, y_pred=forecast, metrics=[mean_absolute_error, mean_squared_error, 'mape'])\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: 可视化预测曲线。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HyperTS支持两种方式绘制预测曲线，一种是可互动的(需要安装plotly)，一种是不可互动的(需要安装matplotlib)，默认是互动的，如果想绘制非互动的，可设置参数```interactive=False```。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "mode": "lines",
         "name": "Lower Bound",
         "type": "scatter",
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Actual vs Forecast",
         "x": 0.5
        },
        "width": 1000,
        "xaxis": {
         "rangeslider": {
          "visible": true
         },
         "title": {
          "text": "Date"
         }
        },
        "yaxis": {
         "title": {
          "text": "Var_3"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(forecast=forecast, actual=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(forecast=forecast, actual=test_data, interactive=False)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "29dd11bf16d40fe19950dcc2f06dd773fb6bc4491ac296fb211bfed7a4a532da"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}